Automatic pest identification system in the greenhouse based on deep learning and machine vision.

Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses.

[Wenweishu Capsule alleviates gastric mucosal lesion in rats with chronic gastritis by inhibiting NF-κB pathway].

Objective To investigate the effect of Wenweishu Capsule on the expression of nuclear factor kappa B (NF-κB)-related proteins in chronic gastritis model rats. Methods Wistar rat models of chronic gastritis were constructed by alternant administrations of sodium deoxycholate, ammonia, alcohol solution and the hunger disorder method. The rats were randomly divided into control group, model group, vatacoenayme group, high-, middle- and low-dose Wenweishu Capsule groups. The control group and model group were treated with normal saline (2 mL/d). The other groups were separately treated with 0.3 g/kg vatacoenayme and 0.76, 0.38, 0.19 g/kg Wenweishu Capsule for 4 weeks. Naked eye observation and HE staining were used to evaluate the pathological changes of gastric tissue. ELISA was performed to measure the levels of tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) in the serum. Immunofluorescence staining was employed to observe the expression of NF-κBp65, inhibitor of NF-κBα (IκBα) in the gastric tissue. Simple Western was utilized to detect the protein levels of NF-κBp65, IκBα and COX2 in the gastric tissue. Results Compared with the control group, the model group was found with thinner gastric mucosa, disappeared or shallower folds, obviously infiltrated mucosa inflammatory cells, disordered glands, and significantly increased levels of serum inflammatory cytokines and NF-κBp65, IκBα and COX2 proteins in the gastric tissue. Compared with the model group, the vatacoenayme group, high- and middle-dose Wenweishu Capsule groups showed the alleviated gastric cavity signs, improved histopathological changes, reduced levels of TNF-α and IL-6 in the serum, and decreased expression of NF-κBp65, IκBα and COX2 proteins in the gastric tissue. Conclusion Wenweishu Capsule can reduce the levels of serum inflammatory factors by inhibiting NF-κB pathway in the gastric tissue, so as to alleviate the injury of gastric mucosa in rats with chronic gastritis.